1. Field of the Invention
The present invention relates to systems and methods for predicting credit ratings transitions conditional on macroeconomic factors and rating facts. In particular, the systems and methods may be used for predicting credit rating transition probabilities, including default probabilities, over multiple horizons at the issuer or portfolio level.
2. Discussion of the Related Art
The measurement of default risk has been one of the major advances in finance in the last decade. Managers of a portfolio of obligations subject to default risk are interested in the average or expected loss associated with the portfolio, and the range of possible losses surrounding that expectation, which constitutes the true credit risk of the portfolio.
Default studies of issuers of corporate bonds have yielded three principal results. The first is an estimate of the default rate associated with a given rating class. The second is an estimate of the variance of that default rate over time. The third are estimates of the probabilities not only of default, but also of changing to any other possible rating from a given initial rating. These estimates of “transition” probabilities are summarized for all initial rating classes in a rectangular table called the transition matrix. Examples are shown in FIGS. 1A and 1B. Bond default rate data are used to assign loss rates to classes of borrowers and to determine the loss risk associated with a single borrower, sometimes by reference to the default rate alone and sometimes by reference to the transition probabilities.
Risk management systems used by market participants take a transition matrix, such as the examples shown in FIGS. 1A and 1B, as an input to model credit risk. In addition to studying default risk, portfolio managers are interested in understanding how changes in economic conditions are going to affect the expected loss of their portfolios. These risk management systems have had difficulty incorporating macroeconomic assumptions or producing a transition matrix conditional on the economy.
Historical approaches to predicting future credit rating transitions have had several limitations. In general, these approaches are limited as a predictor of future rating transitions because actual historical transitions are correlated to economic conditions (pro-cyclical) and generally influenced by, not only the current rating category, but also the path of historical rating changes (non-Markovian). As evidence of actual transitions being pro-cyclical, FIG. 2A compares the one year default rates with the one year change in the U.S. unemployment rate. In this example, the default rate for a given pool of issuers over a given horizon is the share of those issuers which are observed to enter default at some point within that horizon. No adjustment for withdrawal is made. As evidence of actual transitions being generally non-Markovian, FIG. 2B compares Kaplan-Meier estimates of the cumulative probability of downgrading for newly issued single-B issuers, those just downgraded, and those just upgraded The probability of downgrading further is substantially higher for those credits which were just downgraded themselves, and substantially lower for upgraded issuers.
More specifically, a first limitation of historical approaches has been that the historical average default rate can deviate significantly from the actual default rate even when all loans within a grade have the same default rate. Similarly, the historical transition probabilities can deviate significantly from the actual transition probabilities. One reason for this is because the historical approaches fail to take into account macroeconomic factors. Credit ratings are only intended as relative assessments of expected loss. They are not intended to capture a particular default probability over a particular horizon. As shown in FIG. 3, default rates rise and fall over time within a rating category and the default cycle is strongly correlated with economic cycle. FIG. 3 shows a correlation between U.S. recessions and the cycle of the default rate. Credit ratings have proven ineffective at addressing a particular default probability of an issuer, and the changes that occur in the default probability as the economic cycle changes.
Another limitation is that there are substantial differences of default rate within a bond rating grade, with some bonds in a higher grade having greater default rates than some bonds in a lower grade, i.e., there is overlap in default probability ranges. The overlap can be substantial. For instance, there are some Baa rated bonds with Aa default rates and some with B default rates. The overlap in default probability ranges appears to be caused by a lack of timeliness in upgrade and downgrade decisions. Further, the lack of timeliness in rating changes causes a significant bias in transition probabilities. The probability of remaining at the same quality is overstated by about double for most grades, whereas the probabilities for changing to other non-default grades are significantly understated.
In addition, a third limitation is that the range of default rates within a rating grade can cause the mean default rate to significantly exceed the median default rates within a grade. Specifically, the mean may be almost twice as large as the median, and as many as 75% of the borrowers within a rating grade may have default rates that are less than the mean. In short, historical default rates are statistics for the mean default rate, and thus may be biased upwards by as much as double from the typical default rate within the grade.
More sophisticated models of rating transition in general, and default in particular, have been advanced which address these limitations. Most share the drawback that they are horizon dependent, such that obtaining transition probabilities for different horizons requires different models. This is at least statistically inefficient, and at worst may yield contradictory forecasts. In addition, prior art models do not consider withdrawal and do not typically adjust default probabilities based on withdrawal of issuers. This is problematic, as unadjusted numbers as estimates of the “five year default rate” for an exposure which was going to last at least five years, underestimate the true risk by, in some cases, nearly half. Finally, none of the models described in the prior art are capable of producing baseline transition intensities and rating transitions other than default, conditioning on macroeconomic factors, and flexibly conditioning on additional factors.
There remains a need for systems and methods for predicting credit transitions that address one or more of the limitations of the prior art. There remains a need for a system and method of rating transitions conditional on the economy and other rating facts that may be applied at multiple horizons of time and includes a complete set of rating transitions. There is a need for a system and method for generating an accurate forecast of withdrawal for the purpose of forecasting a withdrawal adjusted default rate. There is also a need for a system and method for applying the credit transition model to pertinent rating facts and a future path of macroeconomic drivers, such as unemployment rates and high yield spreads, and outputting a display of issuer or portfolio level reports, including rating migration matrices, marginal default rates, cumulative default rates, first passage probabilities, default distributions, fallen angel rates, rising star rates, forecasted ratings, drill-through reports, and/or summary reports, or any one or combination of the foregoing.